IP Library › Granted Patent US 12,711,559
Granted Patent B2
US 12,711,559 · App. 18/337,024 · Granted Aug 18, 2026

Intelligent orchestration systems for energy and power management within defined domains

Inventors: Charles H. Cella (Pembroke, MA); Andrew Cardno (San Diego, CA)
Assignee: Strong Force EE Portfolio 2022, LLC
G06Q50/06G01R21/133G05B13/0265G05B13/04G05B13/042G05B19/042G06F1/26G06N3/08G06N5/043G06N10/00G06N20/00G06Q10/067G06Q30/018G06Q50/02G06Q50/26H02J3/003H02J3/004H02J3/17H02J3/32H02J3/381H02J13/10H02J13/12H04L41/0833H04L41/145H04L41/16G05B2219/2639G06F30/27G06Q2220/00H02J2101/40H02J2103/30H02J2103/35
View Patent ↗
Loading inventors, assignments & file history…
Monitor This Case
Get email alerts when status or documents change.
Order Certified Copies
Most orders are placed with the USPTO same day — all within 24 business hours.
Order via The Patent Place →
Pre-filled with this patent's details
Quick Facts
Patent No.
US 12,711,559
App. No.
18/337,024
Filed
Jun 18, 2023
Granted
Aug 18, 2026
Kind
B2
Art Unit
2116
USPC
700/295
Abstract

Disclosed herein are AI-based platforms for enabling intelligent orchestration and management of power and energy. In various embodiments, an artificial intelligence system that is trained on a set of energy generation, energy storage, energy delivery and/or energy consumption outcomes, wherein the artificial intelligence system is configured to analyze a data set of current energy generation, current energy storage, current energy delivery and/or current energy consumption information and provide a recommendation including at least one operating parameter that satisfies both of a mobile entity energy demand or a fixed location energy demand in a defined domain. In some embodiments, the operating parameter indicates a generation instruction for a set of energy generation resources, a storage instruction for a set of energy storage resources, a delivery instruction for a set of energy delivery resources, and/or a consumption instruction for a set of entities that consume energy.

Claims (146)

1 . An artificial-intelligence-based (AI-based) platform for enabling intelligent orchestration and management of power and energy, the AI-based platform comprising:

an artificial intelligence system that is trained on a set of energy outcomes, wherein,

the set of energy outcomes includes at least one of an energy generation outcome, an energy storage outcome, an energy delivery outcome, or an energy consumption outcome, and

the artificial intelligence system is configured to,

analyze a set of energy data, wherein the set of energy data includes at least one of current energy generation information, current energy storage information, current energy delivery information, or current energy consumption information,

provide a recommendation including at least one operating parameter that satisfies at least one of a mobile entity energy demand or a fixed location energy demand in a defined domain, and

automatically update a set of consumption points based on the at least one operating parameter, and

an adaptive energy digital twin that is configured to generate an indicator of energy consumption in the set of consumption points, wherein,

the set of consumption points includes at least one of a machine, a factory, or a vehicle in a vehicle fleet, and

the indicator is at least one of a visual indicator or an analytic indicator.

2 . The AI-based platform of claim 1 , wherein the defined domain includes a defined geolocation and a defined time period.

3 . The AI-based platform of claim 1 , wherein the at least one operating parameter indicates a generation instruction for a set of energy generation resources.

4 . The AI-based platform of claim 1 , wherein the at least one operating parameter indicates a storage instruction for a set of energy storage resources.

5 . The AI-based platform of claim 1 , wherein the at least one operating parameter indicates a delivery instruction for a set of energy delivery resources.

6 . The AI-based platform of claim 1 , wherein the at least one operating parameter indicates a consumption instruction for a set of entities that consume energy.

7 . The AI-based platform of claim 1 , wherein,

the artificial intelligence system is configured to adapt a transport of data over at least one of a network or a communication system, and

the adapting is based on at least one of,

a congestion condition,

a delay condition,

a latency condition,

a packet loss condition,

an error rate condition,

a cost of transport condition,

a quality-of-service (QoS) condition,

a usage condition,

a market factor condition, or

a user configuration condition.

8 . The AI-based platform of claim 1 , further comprising an adaptive energy digital twin that represents at least one of,

an energy stakeholder entity,

an energy distribution resource,

a stakeholder information technology,

a networking infrastructure entity,

an energy-dependent stakeholder production facility,

a stakeholder transportation system,

a market condition, or

an energy usage priority condition.

9 . The AI-based platform of claim 1 , further comprising an adaptive energy digital twin that is configured to perform at least one of,

providing a visual indicator of energy consumption by at least one energy consumer,

providing an analytic indicator of energy consumption by at least one energy consumer,

filtering energy data,

highlighting energy data, or

adjusting energy data.

10 . The AI-based platform of claim 1 , wherein the artificial intelligence system is configured to perform at least one of,

extracting energy-related data,

detecting errors in energy-related data,

correcting errors in energy-related data,

transforming energy-related data,

converting energy-related data,

normalizing energy-related data,

cleansing energy-related data,

parsing energy-related data,

detecting patterns in energy-related data,

detecting content in energy-related data,

detecting objects in energy-related data,

compressing energy-related data,

streaming energy-related data,

filtering energy-related data,

loading energy-related data,

storing energy-related data,

routing energy-related data,

transporting energy-related data, or

maintaining security of energy-related data.

11 . The AI-based platform of claim 1 , wherein, the data set is based on at least one public data resource, and the at least one public data resource including at least one of,

a weather data resource,

a satellite data resource,

a census data resource,

population data resource,

demographic data resource,

psychographic data resource,

a market data resource, or

an ecommerce data resource.

12 . The AI-based platform of claim 1 , wherein, the data set is based on at least one enterprise data resource, and the at least one enterprise data resources including at least one of,

resource planning data,

sales data,

marketing data,

financial planning data,

demand planning data,

supply chain data,

procurement data,

pricing data,

customer data,

product data, or

operating data.

13 . The AI-based platform of claim 1 , wherein, the artificial intelligence system is trained based on a training data set, and the training data set is based on at least one of,

at least one human tag,

at least one human label,

at least one human interaction with a hardware system,

at least one human interaction with a software system,

at least one outcome,

at least one AI-generated training data sample,

a supervised learning training process,

a semi-supervised learning training process, or

a deep learning training process.

14 . The AI-based platform of claim 1 , wherein,

the artificial intelligence system is configured to orchestrate delivery of energy to at least one point of consumption, and

the delivery of the energy includes at least one of,

at least one fixed transmission line,

at least one instance of wireless energy transmission,

at least one delivery of fuel, or

at least one delivery of stored energy.

15 . The AI-based platform of claim 1 , wherein,

the artificial intelligence system is configured to record, in a distributed ledger, at least one energy-related event, and

the at least one energy-related event includes at least one of,

an energy purchase event,

an energy sale event,

a service charge associated with an energy purchase event,

a service charge associated with an energy sale event,

an energy consumption event,

an energy generation event,

an energy distribution event,

an energy storage event,

a carbon emission production event,

a carbon emission abatement event,

a renewable energy credit event,

a pollution production event, or

a pollution abatement event.

16 . The AI-based platform of claim 1 , wherein,

the artificial intelligence system is deployed in an off-grid environment, and

the off-grid environment includes at least one of,

an off-grid energy generation system,

an off-grid energy storage system, or

an off-grid energy mobilization system.

17 . The AI-based platform of claim 1 , wherein, the artificial intelligence system is located in proximity to at least one entity configured to at least one of generate energy, store energy, deliver energy, or use energy.

18 . The AI-based platform of claim 1 , wherein,

the artificial intelligence system provides information about at least one of an energy state or an energy flow of at least one entity, and

the at least one entity is configured to at least one of generate energy, store energy, deliver energy, or use energy.

19 . The AI-based platform of claim 1 , wherein,

the artificial intelligence system governs at least one sensor of a set of sensors, and

the set of sensors is associated with a set of infrastructure assets that is configured to at least one of generate energy, store energy, deliver energy, or use energy.

20 . A method for enabling intelligent orchestration and management of power and energy, the method comprising:

controlling an artificial intelligence system that is trained on a set of energy outcomes, wherein,

the set of energy outcomes includes at least one of an energy generation outcome, an energy storage outcome, an energy delivery outcome, or an energy consumption outcome, and

the artificial intelligence system is configured to,

analyze a set of energy data, wherein the set of energy data includes at least one of current energy generation information, current energy storage information, current energy delivery information, or current energy consumption information,

provide a recommendation including at least one operating parameter that satisfies at least one of a mobile entity energy demand or a fixed location energy demand in a defined domain, and

automatically update a set of consumption points based on the at least one operating parameter, and

an adaptive energy digital twin that is configured to generate an indicator of energy consumption in the set of consumption points, wherein,

the set of consumption points includes at least one of a machine, a factory, or a vehicle in a vehicle fleet, and

the indicator is at least one of a visual indicator or an analytic indicator.

21 . The method of claim 20 , wherein the defined domain includes a defined geolocation and a defined time period.

22 . The method of claim 20 , wherein the at least one operating parameter indicates at least one of:

a generation instruction for a set of energy generation resources,

a storage instruction for a set of energy storage resources,

a delivery instruction for a set of energy delivery resources, or

a consumption instruction for a set of entities that consume energy.

Assignments (1)
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Jul 13, 2023
From: CELLA, CHARLES H.; CARDNO, ANDREW
To: STRONG FORCE EE PORTFOLIO 2022, LLC
Reel/Frame 064247/0464 →
Continuity (8)
Continuation PCTUS2022050924 · Nov 23, 2022
Continuation In Part PCTUS2022050932 · Nov 23, 2022
Provisional Application 63375225 · Sep 10, 2022
Provisional Application 63302016 · Jan 21, 2022
Provisional Application 63299727 · Jan 14, 2022
Provisional Application 63291311 · Dec 17, 2021
Provisional Application 63282510 · Nov 23, 2021
Related Publication 20230333522A1 · Oct 19, 2023
References Cited (115)
US 6591255B1 · Tatum · 2003 [cited by applicant]
US 8401708B2 · Nagata · 2013 [cited by applicant]
US 10938634B1 · Cruise · 2021 [cited by applicant]
US 11007891B1 · Kamal · 2021 [cited by applicant]
US 11431170B1 · Guo · 2022 [cited by examiner]
US 11481711B2 · Hodges · 2022 [cited by applicant]
US 11676219B2 · Cella · 2023 [cited by applicant]
US 11816540B2 · Stadler · 2023 [cited by applicant]
US 20020073086A1 · Thompson · 2002 [cited by applicant]
US 20040225648A1 · Ransom · 2004 [cited by applicant]
US 20050143953A1 · Retsina · 2005 [cited by applicant]
US 20090281674A1 · Taft · 2009 [cited by applicant]
US 20100238003A1 · Chan · 2010 [cited by applicant]
US 20100305889A1 · Tomlinson, Jr. · 2010 [cited by applicant]
US 20110046798A1 · Imes · 2011 [cited by applicant]
US 20110166889A1 · Bain · 2011 [cited by applicant]
US 20110172841A1 · Forbes, Jr. · 2011 [cited by applicant]
US 20110205033A1 · Bandyopadhyay · 2011 [cited by applicant]
US 20110231028A1 · Ozog · 2011 [cited by applicant]
US 20120029897A1 · Cherian et al. · 2012 [cited by applicant]
US 20130030581A1 · Luke · 2013 [cited by applicant]
US 20130238266A1 · Savvides · 2013 [cited by applicant]
US 20130274936A1 · Donahue et al. · 2013 [cited by applicant]
US 20140277599A1 · Pande · 2014 [cited by applicant]
US 20140277788A1 · Forbes, Jr. et al. · 2014 [cited by applicant]
US 20150268674A1 · Mucignat · 2015 [cited by applicant]
US 20150294308A1 · Pauker · 2015 [cited by applicant]
US 20150301548A1 · Goparaju · 2015 [cited by applicant]
US 20160190805A1 · Steven · 2016 [cited by applicant]
US 20160204606A1 · Matan · 2016 [cited by applicant]
US 20160333854A1 · Lund · 2016 [cited by applicant]
US 20170005515A1 · Sanders · 2017 [cited by applicant]
US 20170263111A1 · Deluliis · 2017 [cited by applicant]
US 20170279834A1 · Vasseur et al. · 2017 [cited by applicant]
US 20170358041A1 · Forbes, Jr. et al. · 2017 [cited by applicant]
US 20180052431A1 · Shaikh et al. · 2018 [cited by applicant]
US 20180284758A1 · Cella et al. · 2018 [cited by applicant]
US 20180299852A1 · Orsini · 2018 [cited by applicant]
US 20190005165A1 · Meagher · 2019 [cited by applicant]
US 20190026359A1 · Park · 2019 [cited by applicant]
US 20190033845A1 · Cella · 2019 [cited by applicant]
US 20190041842A1 · Cella et al. · 2019 [cited by applicant]
US 20190074693A1 · Kudo · 2019 [cited by applicant]
US 20190109891A1 · Paruchuri · 2019 [cited by applicant]
US 20190138662A1 · Deutsch et al. · 2019 [cited by applicant]
US 20190158309A1 · Park · 2019 [cited by applicant]
US 20190202414A1 · Shih · 2019 [cited by applicant]
US 20190313024A1 · Sellinger et al. · 2019 [cited by applicant]
US 20190340645A1 · Cella · 2019 [cited by applicant]
US 20190347358A1 · Mishra et al. · 2019 [cited by applicant]
US 20190372345A1 · Bain et al. · 2019 [cited by applicant]
US 20200012966A1 · Nagaraju · 2020 [cited by applicant]
US 20200027096A1 · Cooner · 2020 [cited by applicant]
US 20200050612A1 · Bhattacharjee · 2020 [cited by applicant]
US 20200133257A1 · Cella et al. · 2020 [cited by applicant]
US 20200142365A1 · Sharma · 2020 [cited by examiner]
US 20200175551A1 · Penilla · 2020 [cited by applicant]
US 20200257253A1 · Yamaguchi · 2020 [cited by applicant]
US 20200272664A1 · Aggour · 2020 [cited by applicant]
US 20200272899A1 · Dunne · 2020 [cited by applicant]
US 20200274389A1 · Islam · 2020 [cited by applicant]
US 20200334609A1 · Pecenak · 2020 [cited by applicant]
US 20200372104A1 · Calix · 2020 [cited by applicant]
US 20200372588A1 · Shi · 2020 [cited by applicant]
US 20210018205A1 · Ellis · 2021 [cited by applicant]
US 20210021126A1 · Hall · 2021 [cited by applicant]
US 20210090185A1 · Forbes, Jr. · 2021 [cited by applicant]
US 20210109584A1 · Bernat et al. · 2021 [cited by applicant]
US 20210110262A1 · Sebastian · 2021 [cited by applicant]
US 20210110310A1 · Bernat et al. · 2021 [cited by applicant]
US 20210118067A1 · Muenz · 2021 [cited by applicant]
US 20210157312A1 · Cella · 2021 [cited by applicant]
US 20210334914A1 · Sadot · 2021 [cited by applicant]
US 20210342339A1 · Renner · 2021 [cited by applicant]
US 20210342836A1 · Cella · 2021 [cited by applicant]
US 20210383468A1 · Faye · 2021 [cited by examiner]
US 20220044117A1 · Song · 2022 [cited by applicant]
US 20220172206A1 · Cella · 2022 [cited by applicant]
US 20220198562A1 · Cella · 2022 [cited by applicant]
US 20220294217A1 · Spalt · 2022 [cited by applicant]
US 20220366494A1 · Cella · 2022 [cited by applicant]
US 20220410750A1 · Mangal · 2022 [cited by examiner]
US 20230006444A1 · Bain · 2023 [cited by applicant]
US 20230162123A1 · Kagan · 2023 [cited by applicant]
US 20230251291A1 · Cella · 2023 [cited by applicant]
US 20230275438A1 · Lytle · 2023 [cited by applicant]
US 20230289901A1 · Gray · 2023 [cited by examiner]
US 20240106233A1 · Cella · 2024 [cited by applicant]
US 20240106268A1 · Cella · 2024 [cited by applicant]
US 20240141787A1 · Lewis · 2024 [cited by applicant]
CN 107742900A · 2018 [cited by applicant]
CN 111799840A · 2020 [cited by applicant]
CN 112685472A · 2021 [cited by applicant]
CN 113221456A · 2021 [cited by applicant]
EP 3879421A1 · 2021 [cited by applicant]
WO 2020018421A1 · 2020 [cited by applicant]
“EcoStruxure Microgrid Solutions for Small & Medium Buildings—Technical Specification (TEC01),” Schneider Electric, V1.0, Sep. 2020. [cited by applicant]
“EcoStruxure Microgrid Solution for Small &Medium Buildings,” Schneider Electric, 2018. [cited by applicant]
Rossi, et al., “Embedded smart sensor device in construction site machinery,” Computers in industry 108 (2019): 12-20, Jun. 2019, Retrieved on Jan. 31, 2023 from <https://www.sciencedirect.com/science/article/abs/pii/S0… [cited by applicant]
WIPO, International Search Report and Written Opinion for PCT/US2022/050932, dated Feb. 27, 2023, 12 pages. [cited by applicant]
Siemens, Interactive Grid Edge, https://new.siemens.com/content/dam/internet/siemens-com/global/company/topic-areas/smart-infrastructure/grid-edge/application-pages/8794_grid-edge-interactive_201113/, accessed on Oct. 2… [cited by applicant]
Thompson, Rick, “The Grid Edge: How Will Utilities, Vendors and Energy Service Providers Adapt?”, Greentech Media, Oct. 7, 2013. [cited by applicant]
CB Insights, State of Engergy Global 2021 Report. [cited by applicant]
World Economic Forum, “The Future of Electricity: New Technologies Transforming the Grid Edge”, Mar. 2017. [cited by applicant]
Deloitte Center for Energy Solutions, “Supercharged: Challenges and opportunities in global battery storage markets”, 2018. [cited by applicant]
Accenture, “Digital Transformation”, https://www.accenture.com/us-en/insights/digital-transformation-index accessed on Oct. 2, 2023. [cited by applicant]
Urazayev Damir, Bragin Dmitriy; Zykov Dmitriy, Hafizov Rashit, Pospelova Irina, Shelupanov Alexander, “Distributed Energy Management System with the Use of Digital Twin,” 2019 International Multi-Conference on Engineeri… [cited by applicant]
Zekić-Sušac Marijana, et al., “Machine learning based system for managing energy efficiency of public sector as an approach towards smart cities,” International Journal of Information Management, Elsevier Science Ltd., … [cited by applicant]
Widodo D A, et al., “Renewable energy power generation forecasting using deep learning method,” IOP Conference Series: Earth and Environmental Science, vol. 700, No. 1, doi:10.1088/1755-1315/700/1/012026, ISSN 1755-1307… [cited by applicant]
Nammouchi A, et al., “Integration of AI, IoT and Edge-Computing for Smart Microgrid Energy Management,” IEEE International Conference on Environment and Electrical Engineering and 2021 IEEE Industrial and Commercial Pow… [cited by applicant]
Chen A, et al., “Distributed Cooperative Energy Management in Smart Microgrids with Solar Energy Prediction,” IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGr… [cited by applicant]
WIPO, International Search Report for PCT/US2022/050924, dated Jun. 23, 2023. [cited by applicant]
WIPO, Writen Opinion for PCT/US2022/050924, dated Jun. 23, 2023. [cited by applicant]
Zaouali et al., “Smart Home Resource Management based on Multi-Agent System Modeling Combined with SVM Machine Learning for Prediction and Decision-Making”, 2018, ACHI, pp. 1-8 (hereinafter Zaouali) (Year: 2018). [cited by applicant]
Nammouchi et al., “Integration of AI, IoT and Edge-Computing for Smart Microgrid Energy Management”, Sep. 2021, IEEE International Conference, pp. 1-6 (Year: 2021). [cited by applicant]